IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i5p133-d803426.html
   My bibliography  Save this article

Measuring Ethical Values with AI for Better Teamwork

Author

Listed:
  • Erkin Altuntas

    (Cologne Institute for Information Systems, University of Cologne Pohligstrasse 1, 50969 Cologne, Germany)

  • Peter A. Gloor

    (MIT Center for Collective Intelligence, 245 First Street, Cambridge, MA 02142, USA)

  • Pascal Budner

    (Cologne Institute for Information Systems, University of Cologne Pohligstrasse 1, 50969 Cologne, Germany)

Abstract

Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task.

Suggested Citation

  • Erkin Altuntas & Peter A. Gloor & Pascal Budner, 2022. "Measuring Ethical Values with AI for Better Teamwork," Future Internet, MDPI, vol. 14(5), pages 1-28, April.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:133-:d:803426
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/5/133/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/14/5/133/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ronald S. Burt & Robin M. Hogarth & Claude Michaud, 2000. "The Social Capital of French and American Managers," Organization Science, INFORMS, vol. 11(2), pages 123-147, April.
    2. Alex (Sandy) Pentland, 2008. "Honest Signals: How They Shape Our World," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262162563, April.
    3. Susan R Fisk & Jon Overton, 2020. "Bold or reckless? The impact of workplace risk-taking on attributions and expected outcomes," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-19, March.
    4. Gloor, Peter A. & Fronzetti Colladon, Andrea & Grippa, Francesca, 2020. "The digital footprint of innovators: Using email to detect the most creative people in your organization," Journal of Business Research, Elsevier, vol. 114(C), pages 254-264.
    5. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brigitte Granville & Jaume Martorell Cruz & Martha Prevezer, 2015. "Elites, Thickets and Institutions: French Resistance versus German Adaptation to Economic Change, 1945-2015," Working Papers 63, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
    2. Batjargal, Bat, 2007. "Internet entrepreneurship: Social capital, human capital, and performance of Internet ventures in China," Research Policy, Elsevier, vol. 36(5), pages 605-618, June.
    3. Prashant Das & Alan Ziobrowski, 2015. "The Relationship between Indian Realty Stocks and Online Searches," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 14(1), pages 1-19, April.
    4. Hätönen, Jussi, 2011. "The economic impact of fixed and mobile high-speed networks," EIB Papers 7/2011, European Investment Bank, Economics Department.
    5. Hendrik Vollmer, 2013. "What kind of game is everyday interaction?," Rationality and Society, , vol. 25(3), pages 370-404, August.
    6. Jiafeng Gu & Ruiyu Zhu, 2020. "Social Capital and Self-Rated Health: Empirical Evidence from China," IJERPH, MDPI, vol. 17(23), pages 1-15, December.
    7. Monti, Marco & Pelligra, Vittorio & Martignon, Laura & Berg, Nathan, 2014. "Retail investors and financial advisors: New evidence on trust and advice taking heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1749-1757.
    8. Emilio Aguirre & Federico García-Suárez & Gabriela Sicilia, 2021. "Eficiencia técnica en la ganadería de carne bovina pastoril. Medición y exploración de sus determinantes en Uruguay," Documentos de Trabajo (working papers) 1321, Department of Economics - dECON.
    9. Hongjuan Zhang & Rong Han & Liang Wang & Runhui Lin, 2021. "Social capital in China: a systematic literature review," Asian Business & Management, Palgrave Macmillan, vol. 20(1), pages 32-77, February.
    10. Hongjuan Zhang & Rong Han & Liang Wang & Runhui Lin, 0. "Social capital in China: a systematic literature review," Asian Business & Management, Palgrave Macmillan, vol. 0, pages 1-46.
    11. Loftus, Joshua R., 2024. "Position: the causal revolution needs scientific pragmatism," LSE Research Online Documents on Economics 125578, London School of Economics and Political Science, LSE Library.
    12. Bat Batjargal, 2005. "Software Entrepreneurship: Knowledge Networks And Performance Of Software Ventures In China And Russia," William Davidson Institute Working Papers Series wp751, William Davidson Institute at the University of Michigan.
    13. Yamin Du & Huanhuan Cheng & Qing Liu & Song Tan, 2024. "The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    14. Hongjuan Zhang & Liang Wang & Rong Han, 2019. "The China-West divide on social capital: A meta-analysis," Asia Pacific Journal of Management, Springer, vol. 36(3), pages 745-772, September.
    15. Ali Shamsollahi & Danielle A. Chmielewski-Raimondo & Simon J. Bell & Reza Kachouie, 2021. "Buyer–supplier relationship dynamics: a systematic review," Journal of the Academy of Marketing Science, Springer, vol. 49(2), pages 418-436, March.
    16. Nicos Nicolaou & Sue Birley, 2003. "Social Networks in Organizational Emergence: The University Spinout Phenomenon," Management Science, INFORMS, vol. 49(12), pages 1702-1725, December.
    17. Chen, Yong & Lu, Zhiyuan & Liu, Heng & Wang, Hu & Zheng, Zunqing & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based design of target property-oriented fuels using explainable artificial intelligence," Energy, Elsevier, vol. 300(C).
    18. Stefano Cabras & J. D. Tena, 2023. "Implicit institutional incentives and individual decisions: Causal inference with deep learning models," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3739-3754, September.
    19. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    20. Carlos M. Fernández‐Márquez & Francisco J. Vázquez, 2018. "How information and communication technology affects decision‐making on innovation diffusion: An agent‐based modelling approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(3), pages 124-133, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:5:p:133-:d:803426. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.